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Mathematics Institute

Dr Tim Sullivan

1 grey head  

Associate Professor in Predictive Modelling
(Mathematics Institute and School of Engineering)

Co-Director, Warwick Centre for Predictive Modelling

Office: Zeeman Building, C2.10
Phone: +44 (0)24 7615 0294
Email: t.j.sullivan (at) warwick.ac.uk

 

Teaching Responsibilities 2023–2024:

Terms 1–3: Tutorial responsibilities in Mathematics and Engineering

Term 1: ES98A Fundamentals of Predictive Modelling

Term 2: ES3J1 Advanced Systems and Software Engineering and MA3H7 Control Theory

The above modules are likely to remain the same for 2024–2025. See here for information about previous years.

Research Interests

  • uncertainty quantification
  • inverse problems
  • probabilistic numerics
  • data science

Selected Publications

See also this full list of publications.

  1. F. Schäfer, T. J. Sullivan, and H. Owhadi. “Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity.” Multiscale Model. Simul. 19(2):688–730, 2021. doi:10.1137/19M129526XLink opens in a new window
  2. J. Cockayne, C. J. Oates, T. J. Sullivan, and M. Girolami. “Bayesian probabilistic numerical methods.” SIAM Rev. 61(4):756–789, 2019. doi:10.1137/17M1139357Link opens in a new window
  3. H. C. Lie, T. J. Sullivan, and A. L. Teckentrup. “Random forward models and log-likelihoods in Bayesian inverse problems.” SIAM/ASA J. Uncertain. Quantif. 6(4):1600–1629, 2018. doi:10.1137/18M1166523Link opens in a new window
  4. J. Cockayne, C. J. Oates, T. J. Sullivan, and M. Girolami. “Probabilistic numerical methods for PDE-constrained Bayesian inverse problems” in Proceedings of the 36th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, ed. G. Verdoolaege. AIP Conference Proceedings 1853:060001-1–060001-8, 2017. doi:10.1063/1.4985359Link opens in a new window
  5. T. J. Sullivan. Introduction to Uncertainty Quantification, volume 63 of Texts in Applied Mathematics. Springer, 2015. ISBN 978-3-319-23394-9 (hardcover), 978-3-319-23395-6 (e-book). doi:10.1007/978-3-319-23395-6Link opens in a new window
  6. H. Owhadi, C. Scovel, and T. J. Sullivan. “On the brittleness of Bayesian inference.” SIAM Rev. 57(4):566–582, 2015. doi:10.1137/130938633Link opens in a new window
  7. H. Owhadi, C. Scovel, and T. J. Sullivan. “Brittleness of Bayesian inference under finite information in a continuous world.” Elec. J. Stat. 9(1):1–79, 2015. doi:10.1214/15-EJS989Link opens in a new window
  8. T. J. Sullivan, M. McKerns, D. Meyer, F. Theil, H. Owhadi, and M. Ortiz. “Optimal uncertainty quantification for legacy data observations of Lipschitz functions.” ESAIM. Math. Mod. Num. Anal. 47(6):1657–1689, 2013. doi:10.1051/m2an/2013083Link opens in a new window
  9. H. Owhadi, C. Scovel, T. J. Sullivan, M. McKerns, and M. Ortiz. “Optimal Uncertainty Quantification.” SIAM Rev. 55(2):271–345, 2013. doi:10.1137/10080782XLink opens in a new window
  10. M. M. McKerns, L. Strand, T. J. Sullivan, A. Fang, and M. A. G. Aivazis. “Building a Framework for Predictive Science” in Proceedings of the 10th Python in Science Conference (SciPy 2011), June 2011, ed. S. van der Walt and J. Millman. 67–78, 2011. doi:10.25080/Majora-ebaa42b7-00dLink opens in a new window

Personal Homepage

www.tjsullivan.org.ukLink opens in a new window